CVE-2025-8709

Oct. 26, 2025, 6:15 a.m.

7.3
High

Description

A SQL injection vulnerability exists in the langchain-ai/langchain repository, specifically in the LangGraph's SQLite store implementation. The affected version is langgraph-checkpoint-sqlite 2.0.10. The vulnerability arises from improper handling of filter operators ($eq, $ne, $gt, $lt, $gte, $lte) where direct string concatenation is used without proper parameterization. This allows attackers to inject arbitrary SQL, leading to unauthorized access to all documents, data exfiltration of sensitive fields such as passwords and API keys, and a complete bypass of application-level security filters.

Product(s) Impacted

Vendor Product Versions
Langchain-ai
  • Langgraph-checkpoint-sqlite
  • 2.0.10

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-89
Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
The product constructs all or part of an SQL command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended SQL command when it is sent to a downstream component.

*CPE(s)

Affected systems and software identified for this CVE.

Type Vendor Product Version Update Edition Language Software Edition Target Software Target Hardware Other Information
a langchain-ai langgraph-checkpoint-sqlite 2.0.10 / / / / / / /

CVSS Score

7.3 / 10

CVSS Data - 3.0

  • Attack Vector: LOCAL
  • Attack Complexity: LOW
  • Privileges Required: LOW
  • Scope: CHANGED
  • Confidentiality Impact: HIGH
  • Integrity Impact: LOW
  • Availability Impact: NONE
  • CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:N

    View Vector String

Timeline

Published: Oct. 26, 2025, 6:15 a.m.
Last Modified: Oct. 26, 2025, 6:15 a.m.

Status : Received

CVE has been recently published to the CVE List and has been received by the NVD.

More info

Source

security@huntr.dev

*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.